






"""simple docstring"""


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger('transformers.models.encodec')

__SCREAMING_SNAKE_CASE   =   {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
__SCREAMING_SNAKE_CASE   =   {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
__SCREAMING_SNAKE_CASE   =   {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
__SCREAMING_SNAKE_CASE   =   {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
__SCREAMING_SNAKE_CASE   =   {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
__SCREAMING_SNAKE_CASE   =   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__SCREAMING_SNAKE_CASE   =   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__SCREAMING_SNAKE_CASE   =   []
__SCREAMING_SNAKE_CASE   =   []


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for attribute in key.split('.' ):
												lowerCAmelCase   :Optional[Any]						=			getattr(a__     ,      a__ )

						if weight_type is not None:
												lowerCAmelCase   :Dict						=			getattr(a__     ,      a__ ).shape
						else:
												lowerCAmelCase   :Optional[Any]						=			hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
												    F""" {value.shape} for {full_name}""" )

						if weight_type == "weight":
												lowerCAmelCase   :Tuple						=			value
						elif weight_type == "weight_g":
												lowerCAmelCase   :Tuple						=			value
						elif weight_type == "weight_v":
												lowerCAmelCase   :Any						=			value
						elif weight_type == "bias":
												lowerCAmelCase   :Optional[int]						=			value
						elif weight_type == "running_mean":
												lowerCAmelCase   :Dict						=			value
						elif weight_type == "running_var":
												lowerCAmelCase   :List[Any]						=			value
						elif weight_type == "num_batches_tracked":
												lowerCAmelCase   :int						=			value
						elif weight_type == "weight_ih_l0":
												lowerCAmelCase   :Tuple						=			value
						elif weight_type == "weight_hh_l0":
												lowerCAmelCase   :int						=			value
						elif weight_type == "bias_ih_l0":
												lowerCAmelCase   :str						=			value
						elif weight_type == "bias_hh_l0":
												lowerCAmelCase   :str						=			value
						elif weight_type == "weight_ih_l1":
												lowerCAmelCase   :Union[str, Any]						=			value
						elif weight_type == "weight_hh_l1":
												lowerCAmelCase   :int						=			value
						elif weight_type == "bias_ih_l1":
												lowerCAmelCase   :int						=			value
						elif weight_type == "bias_hh_l1":
												lowerCAmelCase   :List[Any]						=			value
						else:
												lowerCAmelCase   :Any						=			value

						logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""" )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						for key in ignore_keys:
												if key.endswith('.*' ):
																		if name.startswith(key[:-1] ):
																								return True
												elif ".*." in key:
																		lowerCAmelCase	, lowerCAmelCase   :List[str]						=			key.split('.*.' )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												lowerCAmelCase   :Any						=			MAPPING_24K
						elif model_name == "encodec_48khz":
												lowerCAmelCase   :int						=			MAPPING_48K
						else:
												raise ValueError(F"""Unsupported model: {model_name}""" )

						for name, value in orig_dict.items():
												if should_ignore(a__     ,      a__ ):
																		logger.info(F"""{name} was ignored""" )
																		continue

												lowerCAmelCase   :str						=			False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			key.split('.*.' )
																								if prefix in name and suffix in name:
																														lowerCAmelCase   :int						=			suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith('embed' ) and name.endswith('embed_avg' ):
																														continue

																								lowerCAmelCase   :Optional[int]						=			True
																								if "*" in mapped_key:
																														lowerCAmelCase   :str						=			name.split(a__ )[0].split('.' )[-2]
																														lowerCAmelCase   :List[str]						=			mapped_key.replace('*'     ,      a__ )
																								if "weight_g" in name:
																														lowerCAmelCase   :Dict						=			'weight_g'
																								elif "weight_v" in name:
																														lowerCAmelCase   :Optional[int]						=			'weight_v'
																								elif "weight_ih_l0" in name:
																														lowerCAmelCase   :Optional[int]						=			'weight_ih_l0'
																								elif "weight_hh_l0" in name:
																														lowerCAmelCase   :Dict						=			'weight_hh_l0'
																								elif "bias_ih_l0" in name:
																														lowerCAmelCase   :Any						=			'bias_ih_l0'
																								elif "bias_hh_l0" in name:
																														lowerCAmelCase   :Optional[int]						=			'bias_hh_l0'
																								elif "weight_ih_l1" in name:
																														lowerCAmelCase   :int						=			'weight_ih_l1'
																								elif "weight_hh_l1" in name:
																														lowerCAmelCase   :Optional[Any]						=			'weight_hh_l1'
																								elif "bias_ih_l1" in name:
																														lowerCAmelCase   :int						=			'bias_ih_l1'
																								elif "bias_hh_l1" in name:
																														lowerCAmelCase   :List[str]						=			'bias_hh_l1'
																								elif "bias" in name:
																														lowerCAmelCase   :List[Any]						=			'bias'
																								elif "weight" in name:
																														lowerCAmelCase   :Union[str, Any]						=			'weight'
																								elif "running_mean" in name:
																														lowerCAmelCase   :Tuple						=			'running_mean'
																								elif "running_var" in name:
																														lowerCAmelCase   :Tuple						=			'running_var'
																								elif "num_batches_tracked" in name:
																														lowerCAmelCase   :List[Any]						=			'num_batches_tracked'
																								else:
																														lowerCAmelCase   :List[Any]						=			None
																								set_recursively(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
																		continue
												if not is_used:
																		unused_weights.append(a__ )

						logger.warning(F"""Unused weights: {unused_weights}""" )


@torch.no_grad()
def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__=None     ,      a__=None     ,      ):





						'''simple docstring'''






						if config_path is not None:
												lowerCAmelCase   :Dict						=			EncodecConfig.from_pretrained(a__ )
						else:
												lowerCAmelCase   :str						=			EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												lowerCAmelCase   :Dict						=			[8, 5, 4, 4]
												lowerCAmelCase   :Dict						=			[2.2]
												lowerCAmelCase   :Union[str, Any]						=			64
												lowerCAmelCase   :Optional[int]						=			3_20_00
												lowerCAmelCase   :List[str]						=			20_48
												lowerCAmelCase   :Any						=			False
												lowerCAmelCase   :Dict						=			False
												lowerCAmelCase   :List[Any]						=			False
						elif model_name == "encodec_48khz":
												lowerCAmelCase   :List[str]						=			[8, 5, 4, 2]
												lowerCAmelCase   :str						=			[3.0, 6.0, 12.0, 24.0]
												lowerCAmelCase   :List[str]						=			4_80_00
												lowerCAmelCase   :Any						=			2
												lowerCAmelCase   :Union[str, Any]						=			False
												lowerCAmelCase   :int						=			'time_group_norm'
												lowerCAmelCase   :Union[str, Any]						=			True
												lowerCAmelCase   :Optional[Any]						=			1.0
												lowerCAmelCase   :Union[str, Any]						=			0.01
						else:
												raise ValueError(F"""Unknown model name: {model_name}""" )

						lowerCAmelCase   :Optional[int]						=			EncodecModel(a__ )

						lowerCAmelCase   :Union[str, Any]						=			EncodecFeatureExtractor(
						    feature_size=config.audio_channels     ,      sampling_rate=config.sampling_rate     ,      chunk_length_s=config.chunk_length_s     ,      overlap=config.overlap     ,      )
						feature_extractor.save_pretrained(a__ )

						lowerCAmelCase   :Any						=			torch.load(a__ )
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												lowerCAmelCase   :List[Any]						=			original_checkpoint['best_state']
						recursively_load_weights(a__     ,      a__     ,      a__ )
						model.save_pretrained(a__ )

						if repo_id:
												print('Pushing to the hub...' )
												feature_extractor.push_to_hub(a__ )
												model.push_to_hub(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		parser.add_argument(
		    '--model',
		    default='encodec_24khz',
		    type=str,
		    help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
		)
		parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)







"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	int   )	->       Tuple:
											if dst_width < 0 or dst_height < 0:
																	raise ValueError('Destination width/height should be > 0'   )

											lowerCAmelCase   :List[Any]						=			img
											lowerCAmelCase   :Optional[int]						=			img.shape[1]
											lowerCAmelCase   :Union[str, Any]						=			img.shape[0]
											lowerCAmelCase   :Optional[int]						=			dst_width
											lowerCAmelCase   :Optional[int]						=			dst_height

											lowerCAmelCase   :Any						=			self.src_w / self.dst_w
											lowerCAmelCase   :Dict						=			self.src_h / self.dst_h

											lowerCAmelCase   :int						=			(
											    np.ones((self.dst_h, self.dst_w, 3)     , np.uinta   ) * 255
											)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																							lowerCAmelCase   :Optional[Any]						=			self.img[self.get_y(UpperCAmelCase   )][self.get_x(UpperCAmelCase   )]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_x * x   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_y * y   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   8_00, 6_00
		__SCREAMING_SNAKE_CASE   =   imread('image_data/lena.jpg', 1)
		__SCREAMING_SNAKE_CASE   =   NearestNeighbour(im, dst_w, dst_h)
		n.process()

		imshow(
		    F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
		)
		waitKey(0)
		destroyAllWindows()